Node Centrality Measures Identify Relevant Structural MRI Features of Subjects with Autism.

Marcello Zanghieri,Gastone Castellani,Daniel Remondini,Giulia Menichetti,Alessandra Retico,Sara Calderoni

doi:10.3390/brainsci11040498

Abstract

Autism spectrum disorders (ASDs) are a heterogeneous group of neurodevelopmental conditions characterized by impairments in social interaction and communication and restricted patterns of behavior, interests, and activities. Although the etiopathogenesis of idiopathic ASD has not been fully elucidated, compelling evidence suggests an interaction between genetic liability and environmental factors in producing early alterations of structural and functional brain development that are detectable by magnetic resonance imaging (MRI) at the group level. This work shows the results of a network-based approach to characterize not only variations in the values of the extracted features but also in their mutual relationships that might reflect underlying brain structural differences between autistic subjects and healthy controls. We applied a network-based analysis on sMRI data from the Autism Brain Imaging Data Exchange I (ABIDE-I) database, containing 419 features extracted with FreeSurfer software. Two networks were generated: one from subjects with autistic disorder (AUT) (DSM-IV-TR), and one from typically developing controls (TD), adopting a subsampling strategy to overcome class imbalance (235 AUT, 418 TD). We compared the distribution of several node centrality measures and observed significant inter-class differences in averaged centralities. Moreover, a single-node analysis allowed us to identify the most relevant features that distinguished the groups.

Highlights

In our investigation, we focused on subjects with autistic disorder; i.e. the subtype with the highest levels of concordance when diagnosed using either the DSM-IV or DSM-5 diagnostic criteria [13]
We focused on a subgroup of the Autism Brain Imaging Data Exchange I (ABIDE-I) dataset, consisting of righthanded males that belong either to the autistic disorder (AUT) or typically developing controls (TD) groups (M = 653 subjects): MAUT = 235
In the Supplementary Materials (Section S3), we show the results of the comparison between a distribution of TD networks and a distribution of AUT networks obtained by a similar subsampling procedure, which are in complete agreement with the analysis shown in the paper

Summary

Introduction

With respect to neurobiological substrates identified using structural magnetic resonance imaging (sMRI), some studies have detected different and specific brain alterations among the DSM-IV-TR subtypes of PDD [8,9]), whereas others identified similar neural underpinnings shared among PDD subgroups [10,11]. These different views were reconciled by a large study that analyzed brain imaging data from the Autism Brain Imaging Data Exchange (ABIDE) cohorts: both common and unique cortical brain areas among Asperger’s, PDD-NOS, and autistic subgroups were found [12]

Methods

Results

Discussion

Conclusion